Sornapudi Sudhir, Stanley R Joe, Stoecker William V, Long Rodney, Xue Zhiyun, Zuna Rosemary, Frazier Shellaine R, Antani Sameer
Department of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO, USA.
Stoecker and Associates, Rolla, MO, USA.
J Pathol Inform. 2020 Dec 24;11:40. doi: 10.4103/jpi.jpi_50_20. eCollection 2020.
Cervical cancer is one of the deadliest cancers affecting women globally. Cervical intraepithelial neoplasia (CIN) assessment using histopathological examination of cervical biopsy slides is subject to interobserver variability. Automated processing of digitized histopathology slides has the potential for more accurate classification for CIN grades from normal to increasing grades of pre-malignancy: CIN1, CIN2, and CIN3.
Cervix disease is generally understood to progress from the bottom (basement membrane) to the top of the epithelium. To model this relationship of disease severity to spatial distribution of abnormalities, we propose a network pipeline, DeepCIN, to analyze high-resolution epithelium images (manually extracted from whole-slide images) hierarchically by focusing on localized vertical regions and fusing this local information for determining Normal/CIN classification. The pipeline contains two classifier networks: (1) a cross-sectional, vertical segment-level sequence generator is trained using weak supervision to generate feature sequences from the vertical segments to preserve the bottom-to-top feature relationships in the epithelium image data and (2) an attention-based fusion network image-level classifier predicting the final CIN grade by merging vertical segment sequences.
The model produces the CIN classification results and also determines the vertical segment contributions to CIN grade prediction.
Experiments show that DeepCIN achieves pathologist-level CIN classification accuracy.
宫颈癌是全球影响女性的最致命癌症之一。使用宫颈活检切片的组织病理学检查来评估宫颈上皮内瘤变（CIN）存在观察者间差异。对数字化组织病理学切片进行自动化处理有潜力更准确地将CIN分级从正常到癌前病变程度增加的分级（CIN1、CIN2和CIN3）进行分类。
一般认为宫颈疾病是从上皮底部（基底膜）向上发展的。为了模拟疾病严重程度与异常空间分布之间的这种关系，我们提出了一个网络管道DeepCIN，通过关注局部垂直区域并融合此局部信息来确定正常/CIN分类，从而对高分辨率上皮图像（从全切片图像中手动提取）进行分层分析。该管道包含两个分类器网络：（1）一个横断面的垂直段级序列生成器，使用弱监督进行训练，以从垂直段生成特征序列，以保留上皮图像数据中从下到上的特征关系；（2）一个基于注意力的融合网络图像级分类器，通过合并垂直段序列来预测最终的CIN分级。
该模型产生CIN分类结果，并确定垂直段对CIN分级预测的贡献。
实验表明DeepCIN实现了病理学家级别的CIN分类准确率。
